Up till now, in speech encoding, generally, quantization noise is made hard to be heard by shaping quantization noise in accordance with human perceptual characteristics. For example, in CELP encoding, quantization noise is shaped using a perceptual weighting filter in which the transfer function is expressed by following equation 1.
                    (                  Equation          ⁢                                          ⁢          1                )                                                                                  W            ⁡                          (              z              )                                =                                    A              ⁡                              (                                  z                  /                                      γ                    1                                                  )                                                    A              ⁡                              (                                  z                  /                                      γ                    2                                                  )                                                    ⁢                                  ⁢                                            where              ⁢                                                          ⁢              0                        ≤                          γ              2                        ≤                          γ              1                        ≤                          1              ⁢                                                          ⁢              and              ⁢                                                          ⁢                              A                ⁡                                  (                  z                  )                                                              =                      1            +                                          ∑                                  i                  =                  1                                M                            ⁢                                                          ⁢                                                a                  i                                ⁢                                  z                                      -                    i                                                  ⁢                                                                  ⁢                                  hold                  .                                                                                        [        1        ]            
Equation 1 is equivalent to following equation 2.
                    (                  Equation          ⁢                                          ⁢          2                )                                                                      W          ⁡                      (            z            )                          =                              1            +                                          ∑                                  i                  =                  1                                M                            ⁢                                                          ⁢                                                                    a                    i                                    ⁡                                      (                                          z                      /                                              γ                        1                                                              )                                                                    -                  i                                                                          1            +                                          ∑                                  i                  =                  1                                M                            ⁢                                                          ⁢                                                                    a                    i                                    ⁡                                      (                                          z                      /                                              γ                        2                                                              )                                                                    -                  i                                                                                        [        2        ]            
Here, ai represents the LPC (Linear Prediction Coefficient) element acquired in the process of CELP encoding, and M represents the order of the LPC. γ1 and γ2 are formant weighting coefficients for adjusting the weights of formants in quantization noise. Generally, the values of formant weighting coefficients γ1 and γ2 are empirically determined by listening. However, optimal values of formant weighting coefficients γ1 and γ2 vary according to frequency characteristics such as the spectral slope of a speech signal itself, or according to whether or not formant structures are present in a speech signal, and whether or not harmonic structures are present in a speech signal.
Therefore, techniques are suggested for adaptively changing the values of formant weighting coefficients γ1 and γ2 according to frequency characteristics of an input signal (e.g., see Patent Document 1). In the speech encoding disclosed in Patent Document 1, by adaptively changing the value of formant weighting coefficient γ2 according to the spectral slope of a speech signal, the masking level is adjusted. That is, by changing the value of formant weighting coefficient γ2 based on features of the speech signal spectrum, it is possible to control a perceptual weighting filter and adaptively adjust the weights of formants in quantization noise. Further, formant weighting coefficients γ1 and γ2 influence the slope of quantization noise, and, consequently, γ2 is controlled including both formant weighting and tilt compensation.
Further, techniques are suggested for switching characteristics of a perceptual weighting filter between a background noise period and a speech period (e.g., see Patent Document 2). In the speech encoding disclosed in Patent Document 2, the characteristics of a perceptual weighting filter are switched depending on whether each period in an input signal is a speech period or a background noise period (i.e., inactive speech period). A speech period is a period in which speech signals are predominant, and a background noise period is a period in which non-speech signals are predominant. According to the techniques disclosed in Patent Document 2, by distinguishing between a background noise period and a speech period and switching the characteristics of a perceptual weighting filter, it is possible to perform perceptual weighting filtering suitable for each period of a speech signal.    Patent Document 1: Japanese Patent Application Laid-Open No. HEI7-86952    Patent Document 2: Japanese Patent Application Laid-Open No. 2003-195900